A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A locally adaptive window for signal matching
International Journal of Computer Vision
New feature points based on geometric invariants for 3D image registration
International Journal of Computer Vision
A multiscale approach to image sequence analysis
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Improving the Detection Performance in Semi-automatic Landmark Extraction
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
B-spline signal processing. I. Theory
IEEE Transactions on Signal Processing
Pattern Recognition Letters
3D human face description: landmarks measures and geometrical features
Image and Vision Computing
A pose-independent method for 3D face landmark formalization
Computer Methods and Programs in Biomedicine
3D human face soft tissues landmarking method: An advanced approach
Computers in Industry
Geometry-based 3D face morphology analysis: soft-tissue landmark formalization
Multimedia Tools and Applications
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We introduce a novel multi-step approach to improved detection of 3D anatomical point landmarks in tomographic images. Such landmarks serve as important image features for a variety of 3D medical image analysis tasks (e.g. image registration). Existing approaches to landmark detection, however, often suffer from a rather large number of false detections. Our multi-step approach combines an existing robust 3D detection operator with two different novel approaches to the reduction of false detections, and is applied within a semi-automatic procedure allowing for interactive control by the user. Experimental results obtained for a number of different anatomical landmarks of the human head in 3D CT and MR images demonstrate that both automatic ROI size selection and incorporation of a priori knowledge of the intensity structure at a landmark significantly improve the detection performance. The applicability of semi-automatic landmark extraction is thus considerably improved. We also summarize the results of a validation study in which we compare the performance of semi-automatic landmark extraction with that of a (standard) manual procedure for landmark extraction. As an exemplary application, we consider rigid MR/CT registration. The main result of our study is that compared to a purely manual procedure, semi-automatic landmark extraction (a) significantly reduces the elapsed time for landmark extraction, (b) generally yields registration results of comparable quality, and (c) increases the reproducibility of the results.